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69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import lightning.pytorch as pl
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from omegaconf import OmegaConf
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from nemo.collections.asr.models.ssl_models import SpeechEncDecSelfSupervisedModel
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from nemo.core.config import hydra_runner
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from nemo.utils import logging
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from nemo.utils.exp_manager import exp_manager
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from nemo.utils.trainer_utils import resolve_trainer_cfg
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"""
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# Example of unsupervised pre-training of a model
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```sh
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python speech_pre_training.py \
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# (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
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model.train_ds.manifest_filepath=<path to train manifest> \
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model.validation_ds.manifest_filepath=<path to val/test manifest> \
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trainer.devices=-1 \
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trainer.accelerator="gpu" \
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strategy="ddp" \
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trainer.max_epochs=100 \
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model.optim.name="adamw" \
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model.optim.lr=0.001 \
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model.optim.betas=[0.9,0.999] \
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model.optim.weight_decay=0.0001 \
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model.optim.sched.warmup_steps=2000
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exp_manager.create_wandb_logger=True \
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exp_manager.wandb_logger_kwargs.name="<Name of experiment>" \
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exp_manager.wandb_logger_kwargs.project="<Namex of project>"
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```
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For documentation on fine-tuning, please visit -
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https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations
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When doing supervised fine-tuning from unsupervised pre-trained encoder, set flag init_strict to False
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"""
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@hydra_runner(config_path="../conf/ssl/fastconformer/", config_name="fast-conformer")
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def main(cfg):
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logging.info(f"Hydra config: {OmegaConf.to_yaml(cfg)}")
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trainer = pl.Trainer(**resolve_trainer_cfg(cfg.trainer))
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exp_manager(trainer, cfg.get("exp_manager", None))
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asr_model = SpeechEncDecSelfSupervisedModel(cfg=cfg.model, trainer=trainer)
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# Initialize the weights of the model from another model, if provided via config
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asr_model.maybe_init_from_pretrained_checkpoint(cfg)
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trainer.fit(asr_model)
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if __name__ == "__main__":
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main()
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